IEEE Technology and Society Magazine - June 2021 - 85

may have limited influence within their organizations,
especially if they focus on a very narrow
aspect of AI system development. They may expect
top executives, liability officers, or corporate social
responsibility staff to assess broader social and ethical
issues. Even social scientists and ethicists tapped
specifically for these issues may find themselves
similarly handicapped, perhaps in an external advisory
role without a real say. The result is the " many
hands " problem, where responsibility for managing
AI is distributed and muddled [34].
This question of responsibility is intimately tied
with how organizations structure job responsibilities
and workflow related to AI. A major concern is
that computer scientists and engineers may be functionally
separated from other staff roles likely to be
tasked with thinking about an AI system's broader
implications-such as higher-level business managers,
the C-suite, and corporate social responsibility
and compliance staff.
Such an organizational structure exacerbates the
disciplinary divide, as inevitably, functional separation
of technical and nontechnical experts in
organizations limits the potential to communicate
effectively, understand issues robustly, and respond
to considerations of AI's impacts on well-being [21].
Note that such a problem may persist even for companies
that have proposed AI ethics advisory or governance
boards, if functional separation remains.
Figuring out how to distribute responsibility for AI's
impacts may involve challenging long held assumptions
and re-envisioning organizational processes
and roles, no easy task.
The governance of knowledge
To that end, consideration of knowledge management
practices may constitute one avenue toward
reimagining organizational processes. An effective
knowledge management system requires the creation
of knowledge, its storage and retrieval, transfer of tacit
and explicit knowledge within organizations, and the
how-to of application itself [35]. When an organization
does not have a proper methodology for governing
knowledge about AI's possible ethical implications
along any of these dimensions, the likelihood of
responsible and effective AI practice decreases.
For example, organizations that fail to consider
the complexity of AI and its possible impacts
broadly enough are unlikely to create the knowledge
necessary to guide decision-making and action.
June 2021
Further, even if such knowledge is collected by some
individuals or teams within an organization, it must
be effectively stored (e.g., in documents, and organizational
memory) in a way that is easily retrievable
by the appropriate teams. Moreover, such knowledge
must be carefully translated during retrieval and transfer,
so that the teams that need to make decisions are
able to process the information and learn. The disciplinary
divide suggests the transfer process cannot
be taken for granted. Finally, organizations need processes
for aggregating preferences and making decisions
using this information, as well as tools to guide
action, which we discuss in the next section.
Without these systems in place, various teams
within the organization and in different phases of
development may fail to become aware of possible
ethical dimensions or problems, and may be incapable
of effective decision-making and informing appropriate
stakeholders. For example, if there is no method
of " flagging " specific issues other than the " usual "
suspects in software development, such as bugs and
incomplete versions, various important questions
might be disregarded or simply left unasked.
The challenge of governing knowledge, therefore,
runs across organizational structure, culture, and
technology [36]. Structural elements include software
and engineering development processes [37],
their integration with larger business development
cycles, and organizational incentives and hierarchy.
Organizational culture can be more intangible
and intractable, and is shaped by corporate vision,
employee interaction, and the values that permeate
business and engineering practices. Finally, technology
(construed broadly) addresses the tools used to
create, store, transfer, and apply knowledge. Carefully
chosen tools must be in place to support each
process toward better governing knowledge.
Addressing these issues is a long-standing organizational
challenge, but it reinforces the need for
thinking holistically. An organizational statement
about AI ethics principles or a tool for mitigating
algorithmic bias may be a valuable starting point,
but these components must be conceived of and
incorporated into a broader organizational governance
system. For example, new staff positions and
processes can be created that challenge traditional
disciplinary silos.
Organizational leadership can
incentivize ethical concern and interdisciplinary
interaction to build a better culture, and the stages of
software engineering can be mapped to knowledge
85

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